Classifying Vehicles

Security is a classification problem: Security mechanisms, or combinations of mechanisms, need to distinguish that which they should allow to happen from that which they should deny. Two aspects complicate this task. First, security mechanisms often only solve a proxy problem. Authentication mechanisms, for example, usually distinguish some form of token – passwords, keys, sensor input, etc. – rather than the actual actors. Second, adversaries attempt to shape their appearance to pass security mechanisms. To be effective, a security mechanism needs to cover these adaptations, at least the feasible ones.

An everyday problem illustrates this: closing roads for some vehicles but not for others. As a universal but costly solution one might install retractable bollards, issue means to operate them to the drivers of permitted vehicles, and prosecute abuse. This approach is very precise, because classification rests on an artificial feature designed solely for security purposes.

Simpler mechanisms can work sufficiently well if (a) intrinsic features of vehicles are correlated with the desired classification well enough, and (b) modification of these features is subject to constraints so that evading the classifier is infeasible within the adversary model.

Bus traps and sump busters classify vehicles by size, letting lorries and buses pass while stopping common passenger cars. The real intention is to classify vehicles by purpose and operator, but physical dimensions happen to constitute a sufficiently good approximation. Vehicle size correlates with purpose. The distribution of sizes is skewed; there are many more passenger cars than buses, so keeping even just most of them out does a lot. Vehicle dimensions do not change on the fly, and are interdependent with other features and requirements. Although a straightforward way exists to defeat a bus trap – get a car that can pass – this is too expensive for most potential adversaries and their possible gain from the attack.